pushing files to the repo from the example!

README.md

@@ -7,126 +7,1210 @@ tags:
 model_file: xgb_model_bayse_optimization_00000.bin
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 ---
 
 # Model description
@@ -185,7 +1269,7 @@ The model is trained with below hyperparameters.
 
 The model plot is below.
 
-<style>#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 {color: black;background-color: white;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 pre{padding: 0;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-toggleable {background-color: white;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-estimator:hover {background-color: #d4ebff;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-item {z-index: 1;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-parallel-item:only-child::after {width: 0;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67 div.sk-text-repr-fallback {display: none;}</style><div id="sk-155c4ebd-3ff3-4878-a6ed-b467111c8e67" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,tree_method=&#x27;auto&#x27;, validate_parameters=1, verbosity=None)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="15187887-3eef-43d1-af97-9d744586ed11" type="checkbox" checked><label for="15187887-3eef-43d1-af97-9d744586ed11" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,tree_method=&#x27;auto&#x27;, validate_parameters=1, verbosity=None)</pre></div></div></div></div></div>
+<style>#sk-913bd997-a708-43ce-9527-b1095a5811c6 {color: black;background-color: white;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 pre{padding: 0;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-toggleable {background-color: white;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-item {z-index: 1;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-parallel-item:only-child::after {width: 0;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-913bd997-a708-43ce-9527-b1095a5811c6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-913bd997-a708-43ce-9527-b1095a5811c6" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,tree_method=&#x27;auto&#x27;, validate_parameters=1, verbosity=None)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="7575e916-5d25-4b62-9977-129209196a1f" type="checkbox" checked><label for="7575e916-5d25-4b62-9977-129209196a1f" class="sk-toggleable__label sk-toggleable__label-arrow">XGBClassifier</label><div class="sk-toggleable__content"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,tree_method=&#x27;auto&#x27;, validate_parameters=1, verbosity=None)</pre></div></div></div></div></div>
 
 ## Evaluation Results
 

config.json

@@ -1,191 +1,1816 @@
 {
     "sklearn": {
         "columns": [
-            "mean radius",
-            "mean texture",
-            "mean perimeter",
-            "mean area",
-            "mean smoothness",
-            "mean compactness",
-            "mean concavity",
-            "mean concave points",
-            "mean symmetry",
-            "mean fractal dimension",
-            "radius error",
-            "texture error",
-            "perimeter error",
-            "area error",
-            "smoothness error",
-            "compactness error",
-            "concavity error",
-            "concave points error",
-            "symmetry error",
-            "fractal dimension error",
-            "worst radius",
-            "worst texture",
-            "worst perimeter",
-            "worst area",
-            "worst smoothness",
-            "worst compactness",
-            "worst concavity",
-            "worst concave points",
-            "worst symmetry",
-            "worst fractal dimension"
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+            "x1",
+            "x2",
+            "x3",
+            "x4",
+            "x5",
+            "x6",
+            "x7",
+            "x8",
+            "x9",
+            "x10",
+            "x11",
+            "x12",
+            "x13",
+            "x14",
+            "x15",
+            "x16",
+            "x17",
+            "x18",
+            "x19",
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+            "x25",
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+            "x28",
+            "x29",
+            "x30",
+            "x31",
+            "x32",
+            "x33",
+            "x34",
+            "x35",
+            "x36",
+            "x37",
+            "x38",
+            "x39",
+            "x40",
+            "x41",
+            "x42",
+            "x43",
+            "x44",
+            "x45",
+            "x46",
+            "x47",
+            "x48",
+            "x49",
+            "x50",
+            "x51",
+            "x52",
+            "x53",
+            "x54",
+            "x55",
+            "x56",
+            "x57",
+            "x58",
+            "x59",
+            "x60",
+            "x61",
+            "x62",
+            "x63",
+            "x64",
+            "x65",
+            "x66",
+            "x67",
+            "x68",
+            "x69",
+            "x70",
+            "x71",
+            "x72",
+            "x73",
+            "x74",
+            "x75",
+            "x76",
+            "x77",
+            "x78",
+            "x79",
+            "x80",
+            "x81",
+            "x82",
+            "x83",
+            "x84",
+            "x85",
+            "x86",
+            "x87",
+            "x88",
+            "x89",
+            "x90",
+            "x91",
+            "x92",
+            "x93",
+            "x94",
+            "x95",
+            "x96",
+            "x97",
+            "x98",
+            "x99",
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+            "x126",
+            "x127",
+            "x128",
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+            "x131",
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+            "x134",
+            "x135",
+            "x136",
+            "x137",
+            "x138",
+            "x139",
+            "x140",
+            "x141",
+            "x142",
+            "x143",
+            "x144",
+            "x145",
+            "x146",
+            "x147",
+            "x148",
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+            "x151",
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+            "x153",
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+            "x179",
+            "x180",
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+            "x182",
+            "x183",
+            "x184",
+            "x185",
+            "x186",
+            "x187",
+            "x188",
+            "x189",
+            "x190",
+            "x191",
+            "x192",
+            "x193",
+            "x194",
+            "x195",
+            "x196",
+            "x197",
+            "x198",
+            "x199",
+            "x200",
+            "x201",
+            "x202",
+            "x203",
+            "x204",
+            "x205",
+            "x206",
+            "x207",
+            "x208",
+            "x209",
+            "x210",
+            "x211",
+            "x212",
+            "x213",
+            "x214",
+            "x215",
+            "x216",
+            "x217",
+            "x218",
+            "x219",
+            "x220",
+            "x221",
+            "x222",
+            "x223",
+            "x224",
+            "x225",
+            "x226",
+            "x227",
+            "x228",
+            "x229",
+            "x230",
+            "x231",
+            "x232",
+            "x233",
+            "x234",
+            "x235",
+            "x236",
+            "x237",
+            "x238",
+            "x239",
+            "x240",
+            "x241",
+            "x242",
+            "x243",
+            "x244",
+            "x245",
+            "x246",
+            "x247",
+            "x248",
+            "x249",
+            "x250",
+            "x251",
+            "x252",
+            "x253",
+            "x254",
+            "x255",
+            "x256",
+            "x257",
+            "x258",
+            "x259",
+            "x260",
+            "x261",
+            "x262",
+            "x263",
+            "x264",
+            "x265",
+            "x266",
+            "x267",
+            "x268",
+            "x269",
+            "x270",
+            "x271",
+            "x272",
+            "x273",
+            "x274",
+            "x275",
+            "x276",
+            "x277",
+            "x278",
+            "x279",
+            "x280",
+            "x281",
+            "x282",
+            "x283",
+            "x284",
+            "x285",
+            "x286",
+            "x287",
+            "x288",
+            "x289",
+            "x290",
+            "x291",
+            "x292",
+            "x293",
+            "x294",
+            "x295",
+            "x296",
+            "x297",
+            "x298",
+            "x299",
+            "x300"
         ],
         "environment": [
-            "scikit-learn=1.0.2",
             "xgboost=1.5.2"
         ],
         "example_input": {
-            "area error": [
-                30.29,
-                96.05,
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         "model": {

init_repo_MLstructureMining.py

@@ -17,12 +17,13 @@ from sklearn.experimental import enable_halving_search_cv  # noqa
 from sklearn.model_selection import HalvingGridSearchCV, train_test_split
 import shutil
 from skops import card, hub_utils
-
+from data_loader import get_data_splits_from_clean_data
 # Paths
 model_path = "xgb_model_bayse_optimization_00000.bin"
 label_path = "labels.csv"
 
 
+
 # Data
 X, y = load_breast_cancer(as_frame=True, return_X_y=True)
 X_train, X_test, y_train, y_test = train_test_split(
@@ -38,6 +39,11 @@ param_grid = {
 }
 
 
+train_tuple = get_data_splits_from_clean_data(
+    "./cifs_test_s_trained_model", label_path, simple_load=True, n_data=-1
+)
+print(train_tuple)
+X_test = train_tuple[0]
 
 booster = xgboost.Booster({'nthread': 8})
 booster.load_model(model_path)
@@ -50,11 +56,12 @@ model._Booster = booster
 local_repo = mkdtemp(prefix="skops-")
 hub_utils.init(
     model=model_path,
-    requirements=[f"scikit-learn={sklearn.__version__}", f"xgboost={xgboost.__version__}"],
+    requirements=[f"xgboost={xgboost.__version__}"],
     dst=local_repo,
     task="tabular-classification",
     data=X_test,
 )
+
 shutil.copy(label_path, os.path.join(local_repo, label_path))
 if "__file__" in locals():  # __file__ not defined during docs built
     # Add this script itself to the files to be uploaded for reproducibility
@@ -62,6 +69,8 @@ if "__file__" in locals():  # __file__ not defined during docs built
 
 print(os.listdir(local_repo))
 print(type(model))
+print(card.metadata_from_config(Path(local_repo)))
+print(type(card.metadata_from_config(Path(local_repo))))
 model_card = card.Card(model, metadata=card.metadata_from_config(Path(local_repo)))
 model_card.save(Path(local_repo) / "README.md")